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\newcommand{\hrone}{temporal functionality heuristic}
\newcommand{\hrtwo}{temporal burstiness heuristic}
\newcommand{\hrthree}{one event-mention per discourse heuristic}
\newcommand{\hrfour}{Tense Consistency Heuristic}


\newcommand{\temporal}{Temporal Hypotheses}
\newcommand{\sys}{\mbox{\sc NewsSpike}}   % afar
\newcommand{\kylin}{\mbox{\sc Kylin}}
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%\newcommand{\mtt}[1]{\mbox{$\tt{#1}$}}
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\title{Harvesting Parallel News Streams to Cluster Relation Phrases}

%\title{Distant Supervision for Information Extraction of Polymorphic Tuples}
%\title{Distant Supervision for Information Extraction
%                   without Relational Exlcusivity}


%\author{Congle Zhang, Daniel S. Weld \\
%  Computer Science \& Engineering \\
%  University of Washington\\
%  Seattle, WA 98195, USA \\
%  {\tt \{clzhang,weld\}@cs.washington.edu} \\}
%  Second Author \\
%  Affiliation / Address line 1 \\
%  Affiliation / Address line 2 \\
%  Affiliation / Address line 3 \\
%  {\tt email@domain} \\}

%\date{}

\begin{document}
\maketitle

\begin{abstract}
The distributional hypothesis, which states that words that occur in
similar contexts tend to have similar meanings, has inspired several
Web mining algorithms for clustering semantically equivalent phrases.
%(dirt, curran, resolver, usp, berant?).
Unfortunately, these methods have several drawbacks, such as confusing
synonyms with antonyms and causes with effects. This paper introduces
three Temporal Correspondence Heuristics, that characterize
regularities in parallel news streams, and shows how they may be used
for high precision relation clustering. We encode the heuristics in a
probabilistic graphical model to create the \sys\ algorithm for mining
news streams. We present experiments demonstrating that
\sys\ significantly outperforms several competitive baselines.  In
order to spur further research, we provide a large annotated corpus of
timestamped news articles as well as the clusters produced by \sys.
\end{abstract}

\input{intro}
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%The source code of our system, its output, and all data annotations are available at {\tt http://cs.uw.edu/homes/raphaelh/mr}.
%
%\section*{Acknowledgments}
%We thank Sebastian Riedel and Limin Yao for sharing their data and providing valuable advice.
%This material is based upon work supported by a WRF / TJ Cable Professorship, a gift from Google and
%by the Air Force Research Laboratory (AFRL) under prime contract no. FA8750-09-C-0181.  Any opinions, findings, and conclusion or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the Air Force Research Laboratory (AFRL).

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